Commercial optical human motion capture systems perform well in studio-like environments, but they do not provide solution in daily-life surroundings. Micro-sensor motion capture has shown its potentials because of its ubiquity and low cost. We present an ambulatory low-cost real-time motion capture system using wearable micro-sensors (accelerometers, magnetometers and gyroscopes), which can capture and reconstruct human motion in real-time almost everywhere. It mainly consists of three parts: a sensor subsystem, a data fusion subsystem and an animation subsystem. The sensor subsystem collects human motion signals and transfers them into the data fusion subsystem. The data fusion subsystem performs sensor fusion to obtain motion information, i.e., the orientation and position of each body segment. Using the motion information from the data fusion subsystem, the animation subsystem drives the avatar in the 3D virtual world in order to reconstruct human motion. All the processes are accomplished in real-time. The experimental results show that our system can capture motions and drive animations in real-time vividly without drift and delay. And the output from our system can be made use of in film-making, sports training and argument reality applications, etc.

Visual analysis and exploration of data has been extensively studied in various fields. As the amount of data increases, we need scalable and efficient visualization tools integrated with cross-discipline elements to explore and overcome the issues exposed by the large data sets. In this paper, we present our dynamic linking mechanism to create a visualization framework (mVis, multi-visualizer for multi-dimensional data) for visual exploration of large relational data sets. It is an extendable, flexible, and easy-to-use environment. The linking mechanism links co-operating components and data-connected interactive visualizers to the framework. It constitutes object-and event-linkers which are identified by their names so that new external components can subscribe to any linker group easily without compiling the framework.

Data observations that lie on a manifold can be approximated by a collection of overlapping local patches, the alignment of which in a low dimensional Euclidean space provides an embedding of the data. This paper describes an embedding method using classical multidimensional scaling as a local model based on the fact that a manifold locally resembles an Euclidean space. A set of overlapping neighborhoods are chosen by a greedy approximation algorithm of minimum set cover. Local patches derived from the set of overlapping neighborhoods by classical multidimensional scaling are aligned in order to minimize a residual measure, which has a quadratic form of the resulting global coordinates and can be minimized analytically by solving an eigenvalue problem. This method requires only distances within each neighborhood and provides locally isometric embedding results. The size of the eigenvalue problem scales with the number of overlapping neighborhoods rather than the number of data points. Experiments on both synthetic and real world data sets demonstrate the effectiveness of this method. Extensions and variations of the method are discussed.

Neighborhood graph construction is usually the first step in algorithms for isometric data embedding and manifold learning that cope with the problem of projecting high dimensional data to a low space. This paper begins by explaining the algorithmic fundamentals of techniques for isometric data embedding and derives a general classification of these techniques. We will see that the nearest neighbor approaches commonly used to construct neighborhood graphs do not guarantee connectedness of the constructed neighborhood graphs and, consequently, may cause an algorithm fail to project data to a single low dimensional coordinate system. In this paper, we review three existing methods to construct k-edge-connected neighborhood graphs and propose a new method to construct k-connected neighborhood graphs. These methods are applicable to a wide range of data including data distributed among clusters. Their features are discussed and compared through experiments.

One fundamental problem for visualizing frequent itemsets and association rules is how to present a long border of frequent itemsets in an itemset lattice. Another problem comes from the lack of an effective visual metaphor to represent many-to-many relationships. This work proposes an approach for visualizing frequent itemsets and many-to-many association rules by a novel use of parallel coordinates. An association rule is visualized by connecting items in the rule, one item on each parallel coordinate, with continuous polynomial curves. In the presence of item taxonomy, each coordinate can be used to visualize an item taxonomy tree which can be expanded or shrunk by user interaction. This user interaction introduces a border, which separates displayable itemsets from nondisplayable ones, in the generalized itemset lattice. Only those itemsets that are both frequent and displayable are considered to be displayed. This approach of visualizing frequent itemsets and association rules has the following features: 1) It is capable of visualizing many-to-many rules and itemsets with many items. 2) It is capable of visualizing a large number of itemsets or rules by displaying only those ones whose items are selected by the user. 3) The closure properties of frequent itemsets and association rules are inherently supported such that the implied ones are not displayed. Usefulness of this approach is demonstrated through examples.

This paper discusses 3D visualization and interactive exploration of large relational data sets through the integration of several well-chosen multidimensional data visualization techniques and for the purpose of visual data mining and exploratory data analysis. The basic idea is to combine the techniques of grand tour, direct volume rendering, and data aggregation in databases to deal with both the high dimensionality of data and a large number of relational records. Each technique has been enhanced or modified for this application. Specifically, positions of data clusters are used to decide the path of a grand tour. This cluster-guided tour makes intercluster-distance-preserving projections in which data clusters are displayed as separate as possible. A tetrahedral mapping method applied to cluster centroids helps in choosing interesting cluster-guided projections. Multidimensional footprint splatting is used to directly render large relational data sets. This approach abandons the rendering techniques that enhance 3D realism and focuses on how to efficiently produce real-time explanatory images that give comprehensive insights into global features such as data clusters and holes. Examples are given where the techniques are applied to large (more than a million records) relational data sets.

Visual exploration of large relational datasets is always a challenge for exploratory analysis and has immediate applications in data mining, information classification and information retrieval.n23Tool is a tool designed for interactive exploratory visualization of large relational datasets and data clusters.It employs the grand tour method to view multidimensional data through 3D dynamic projections.A 3D cluster-guided tour could be designed to display data clusters where path of the tour is determined by cluster centroids.The cluster-guided tour makes inter-clusterdistance-preserving projections in the perspectives that clusters are displayed as separate as possible.n23Tool supports the visualization of very large relational datasets by using volume rendering through texture splatting.Only aggregations or parts of data needed for rendering are retrieved from databases.n23Tool can process continuous variables as well as categorical variables.This approach of multidimensional visualization provides a natural metaphor to visualize large datasets and data clusters by mapping the data onto a timeindexed family of 3D linear projections suitable for human eye's exploration.

This paper presents a query language, QPF, and describes its implementation in a knowledge-based geographical information system (KGIS). Using QPF, users can retrieve both symbolic and graphical data, and browse through the entire database without prior knowledge of the contents and organization of the KGIS. Processing of complex queries, such as query by object spatial relations, is guided by knowledge represented in frames that simplifies not only query processing, but also query definition and makes querying user-friendly.